Using ATR-FTIR spectra and convolutional neural networks for characterizing mixed plastic waste

Abstract

We present a convolutional neural network (CNN) framework for classifying different types of plastic materials that are commonly found in mixed plastic waste (MPW) streams. The CNN framework uses experimental ATR-FTIR (attenuated total reflection-Fourier transform infrared spectroscopy) spectra to classify ten different plastic types. An important aspect of this type of spectral data is that it can be collected in real-time; as such, this approach provides an avenue for enabling the high-throughput characterization of MPW. The proposed CNN architecture (which we call PlasticNet) uses a Gramian angular representation of the spectra. We show that this 2-dimensional (2D) matrix representation highlights correlations between different frequencies (wavenumber) and leads to significant improvements in classification accuracy, compared to the direct use of spectra (a 1D vector representation). We also demonstrate that PlasticNet can reach an overall classification accuracy of over 87% and can classify certain plastics with 100% accuracy. Our framework also uses saliency maps to analyze spectral features that are most informative.